Decoder with Nilay Patel
00:25:01 · Joey Brookhart
[00:25:01] S4: Yeah. Mean, I think the deals have always been very creatively structured, and I think the numbers, they've actually only burned 10,000,000,000 of cash. You know, they have a 120,000,000,000 plus of cash and marketable securities on the balance sheet today. You know, post IPO, probably another 100,000,000,000. You know, it's a great deal for both parties. They only really rent to compute. It's not like they have these massive capital outlays or even rent to own. So yeah, and you can make some arguments about like certain accounting and okay, there are these massive commute commitment obligations that's technically, you know, future debt. From a dilution perspective, all tech companies dilute, you know, do they are they going to need to raise another 100,000,000 of debt or equity at some point, you know, possibly? Can they continue to creatively structure and finance, you know, future compute build outs? You know, probably as well. I know there's been a lot of in the last few weeks, a lot of angst about the future of compute financing and data center financing. But if you're Anthropic, if you're a major lab, a hyperscaler, or Broadcom and Nvidia that are backstopping a lot of these deals, that's probably not going to change anytime soon. As an equity investor, would I be worried about getting super diluted? I don't think so. If you believe in this long term AGI bull case and that Anthropic is going to be a leader in this, you're probably looking at a multi 10,000,000,000,000 plus dollar business. You know, I I I think they can outrun dilution at that point.
00:26:17 · Nilay Patel
[00:26:17] S2: Let's talk about the commute... Compute commitments. I saw at least one analysis saying over the next ten years, it... It's 800,000,000,000 plus in commitments to the various hyperscalers and neo clouds. That's a lot. You're saying that they don't have that much money, they haven't even spent that much money yet. Do you see them generating the revenues to pay off those commitments?
00:26:36 · Joey Brookhart
[00:26:36] S4: Yeah. I think when when when people look at the headline commitments, know, the $5,600,000,000,000, you know, number over seven to ten years, let's call it. You have it actually at what they're paying for compute today actually isn't that many gigawatts, you know. If you think about them spending, you know, right now, you know, think on average they're close to 11,000,000 per megawatt in spend, or 11,000,000,000 per gigawatt. You know, if you kind of take that back to the annual level and say, okay, you know, the five twenty numbers over, you know, seven years, for example, that's really not that many gigawatts and they're going be well beyond, you know, when they get to something like 20 gigawatts, that future compute commitments that they're running essentially is going to be well over, you know, well into the trillions. So yes, they are going to have the revenue to do so. I think it's more of how much revenue are they going to have relative to how much compute, and then two, the compute efficiency equation. If they keep getting you know, a lot more revenue out of every inference megawatt, you know, how much do you really want to invest in training and take margins to zero from like a rental perspective? Because you can get to something where they get to even 80, 85% of their fleet is just on training and research. The revenue, yes, it's going to scale pretty well. They're not in any dire, you know, unless, you know, unless we realize like all this revenue disappears or people churn, you know, we're really not in a huge like, it's like they have to hit, you know, a trillion dollars of revenue extremely, extremely quickly, you know, to achieve all this and pay for this.
00:28:00 · Nilay Patel, Joey Brookhart
[00:28:00] S2: Unless you're trying to build digital god and unless it doesn't diffuse across the economy as fast as everyone wants it to. Those are bets, right? [00:28:07] S4: They're huge bets and I think like the numbers you look at if you're really bullish on this ramp of like into the digital economy is, you know, they say within their Fortune 500 customers, they have 50% of the Fortune 500. They are 0.1% of their total customers' operating expenses. I think when you look at that on like a per user basis, it's like $8 a month if you believe OpEx is, you know, doesn't include cost of goods sold, and if you believe it cost, you know, they include a cost of goods sold, you know, it's sub $50 per month. And they said the same thing, of their top 5% of Claude Code users, they're spending $18,000 a year on average, the top 5% of users. Their non Claude Code users spend under $75 a month, well under $75 there's this huge, especially when we talk to enterprises and these traditional buyers of software of IT, penetration is extremely low in like traditional white collar work. And I think that's the big point is, too, there is a large ramp period here of both seats adoption and usage. You know, you can make arguments of who's going to win in this, but it's obvious, you know, the Frontier Labs are going to get some portion of this. It's going to be pretty material to a net new ARR next year in 2027.
00:29:18 · Nilay Patel
[00:29:18] S2: One of the things that's really interesting about that is we talk about the Frontier Labs, we talk about the hyperscalers, Anthropic has a lot of wild risk factors in this S1, including that Claude might be conscious and kill us all. But one of the most direct and maybe easiest to understand is that AWS, Azure and Google Cloud are both suppliers and competitors in some real way, particularly Google. They say, these relationships could turn. Right? They have a big relationship with SpaceX AI. Elon is going to train Grok, they're going to compete with SpaceX AI at some point. That relationship might become precarious. As you say, they're only renting their compute. At some point the market might force them to own more of their compute because their own competitors don't want to lease their capacity to the leading company. How do you see that playing out?
00:30:05 · Joey Brookhart, Nilay Patel
[00:30:05] S4: We definitely see both OpenAI and Anthropic going more direct and owning, not owning the compute, but more rent to own or going through an SPV, you know, that some financial sponsor does and kind of doing the compute more directly versus renting. So there's two points of this, too. Google, from one perspective, is also selling their external chips to customers and saying, hey, we don't care to monetize this within Google Cloud or even allocate the share of, if you think we're very chip constrained, we don't even care that these are going to DeepMind or internal GCP compute for us to monetize. We're just willing to become a chip seller. Anthropic loves TPUs, they have there's 3.5, four gigawatts plus and probably more in the pipeline in terms of orders there of Anthropic buying these chips directly to then rent. That's Right. The market buying [00:30:52] S2: them directly to rent. So they're buying them into a Neo Cloud? [00:30:55] S4: Yeah, they buy them through like an SPV, or the SPV buys them and then they rent it from the SPV. [00:31:00] S2: Got it, okay. [00:31:01] S4: Even within Google Cloud or AWS or Foundry, Anthropic is massive. 48% of their business comes through the Bedrock Foundry and Vertex, where people are paying through their existing cloud relationship to access these models. They're not even talking to they are, but you don't have to interact with the Anthropic Salesforce. [00:31:21] S2: You mentioned this earlier. This is the marketing channel where Anthropic gets rev share.